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January 30, 2013

Big Data: The All-Important "Why" and "How"

Nearly 10,000 have
visited my postings of two blogs on Big Data on a couple sites since January 17.
There’s no question that big data is “hot” and that the business landscape is
shaped by data more than ever before. Google’s Eric Schmidt tells us the world
creates 5 exabytes of data every two days. That’s really big data. (FYI:
Exabyte—Cisco says annual global IP traffic will reach two-thirds of a zettabyte
or 667 exabytes in 2013). But the real big
data question is not how much, but so what?

Recently, the Economist Intelligence Unit, with support
from SAS Software, set out to answer that question. They surveyed 752 senior executives from a
broad range of sectors and countries. They also conducted in-depth interviews
with 17 executives, consultants and specialists regarded as data pioneers.
Fifty-seven percent (57%) of the respondents had been working with big data for
at least three years, and nearly one-half of the respondents said that their
companies are well ahead of their peer firms and have a defined data strategy.

What’s significant about the results is that they are
reflective of a growing consensus in the field of big data. The consensus research
also confirms my blogs on the big data IT fumble and the data scientist as
“sexiest career.” The summary highlights of the research are as follows:

The link between
effective use of big data and financial performance is strong. Top
performing companies, the so-called strategy data managers, process data more
rapidly, see the rewards across all business disciplines, place a higher
premium on data than their peers, collect more data from everywhere and use
data more broadly across the business.

The survey suggests that there is a correlation between
big data and financial performance. But a chief problem with all survey
research is the omission or de-emphasis of other factors. The firms surveyed tended
to be successful firms prior to the use of big data. It’s probable that the big
data leverages other success factors.

Still, it’s arguable that big data is a highly
significant competitive advantage. At this point in its development, firms
without that expertise are liable to be left behind.

Well-defined data
strategy is always the top priority, not collecting and processing information.
One of the intriguing research bits is that 46% of executives from
companies that significantly outperform their peers financially have a
“well-defined” data strategy. Inevitably and always, the strategy should be
based on key business priorities. The role of the data strategy, then, is to
identify the problems the firm wants to solve. But the data component serves
those priorities and, therefore, is developed afterwards.

Strategy is key: big data merely supports
strategy.

Talent matters as
much as technology. Research finds that executives
need to ensure that analytic thinking is not confined to the It department.
Especially valuable is a sidebar, “What’s in an algorithm?” detailing training
that works with employees with little or no knowledge of data science, showing
employees how to create a data plan that results in a series of business
recommendations. Yet, a close reading of
the language of the research conclusion indicates the need for still more
clarity on this issue.

As I emphasized in my blog, Big data: the IT fumble, the
uses of big data emphasize that IT and data science are very different animals.
Any observant employee is aware that when it comes to data or even the use of
the term, data, the ship is listing
way over to the side of IT. The lack of professional data scientists for the
discipline makes the problem even more serious. Data scientists will need to understand the business as well as cognitive and behavioral sciences. One principal at Deloitte
Consulting recommends that companies may need to search academic departments or
consider sharing a data scientist. So it can’t be emphasized enough that people
are at the heart of the discipline, not data.

The biggest gains
from big data impact are in customer-facing areas. The report recommends
that social media and web-tracking technologies are especially useful for
collecting customer data. Loyalty cards and user-generated web content have
already led to significant changes in retail and entertainment. Of course,
consumer products companies have long used customer data supplied by
information and measurement companies like Nielson. Thus, marketing and sales groups
have built-in experience and history of using customer generated data, making
analogies to web-data an easier reach for those firms.

Evidence of the use and advance of data is everywhere. As
the report indicates, an ecosystem of
data-focused companies is springing up and creating new businesses. Still
further, analysts at the McKinsey Global
Institute estimate that just one sector—the US healthcare industry—could create
US$300bn in value annually if it used big data to drive efficiency and quality.

The “why” of big data is fairly obvious. It’s the “how”
that creates and will continue to create frustration, while at the same time
creating significant business success. That’s typical of an evolving
discipline.

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Big Data: The All-Important "Why" and "How"

Nearly 10,000 have
visited my postings of two blogs on Big Data on a couple sites since January 17.
There’s no question that big data is “hot” and that the business landscape is
shaped by data more than ever before. Google’s Eric Schmidt tells us the world
creates 5 exabytes of data every two days. That’s really big data. (FYI:
Exabyte—Cisco says annual global IP traffic will reach two-thirds of a zettabyte
or 667 exabytes in 2013). But the real big
data question is not how much, but so what?

Recently, the Economist Intelligence Unit, with support
from SAS Software, set out to answer that question. They surveyed 752 senior executives from a
broad range of sectors and countries. They also conducted in-depth interviews
with 17 executives, consultants and specialists regarded as data pioneers.
Fifty-seven percent (57%) of the respondents had been working with big data for
at least three years, and nearly one-half of the respondents said that their
companies are well ahead of their peer firms and have a defined data strategy.

What’s significant about the results is that they are
reflective of a growing consensus in the field of big data. The consensus research
also confirms my blogs on the big data IT fumble and the data scientist as
“sexiest career.” The summary highlights of the research are as follows:

The link between
effective use of big data and financial performance is strong. Top
performing companies, the so-called strategy data managers, process data more
rapidly, see the rewards across all business disciplines, place a higher
premium on data than their peers, collect more data from everywhere and use
data more broadly across the business.

The survey suggests that there is a correlation between
big data and financial performance. But a chief problem with all survey
research is the omission or de-emphasis of other factors. The firms surveyed tended
to be successful firms prior to the use of big data. It’s probable that the big
data leverages other success factors.

Still, it’s arguable that big data is a highly
significant competitive advantage. At this point in its development, firms
without that expertise are liable to be left behind.

Well-defined data
strategy is always the top priority, not collecting and processing information.
One of the intriguing research bits is that 46% of executives from
companies that significantly outperform their peers financially have a
“well-defined” data strategy. Inevitably and always, the strategy should be
based on key business priorities. The role of the data strategy, then, is to
identify the problems the firm wants to solve. But the data component serves
those priorities and, therefore, is developed afterwards.

Strategy is key: big data merely supports
strategy.

Talent matters as
much as technology. Research finds that executives
need to ensure that analytic thinking is not confined to the It department.
Especially valuable is a sidebar, “What’s in an algorithm?” detailing training
that works with employees with little or no knowledge of data science, showing
employees how to create a data plan that results in a series of business
recommendations. Yet, a close reading of
the language of the research conclusion indicates the need for still more
clarity on this issue.

As I emphasized in my blog, Big data: the IT fumble, the
uses of big data emphasize that IT and data science are very different animals.
Any observant employee is aware that when it comes to data or even the use of
the term, data, the ship is listing
way over to the side of IT. The lack of professional data scientists for the
discipline makes the problem even more serious. Data scientists will need to understand the business as well as cognitive and behavioral sciences. One principal at Deloitte
Consulting recommends that companies may need to search academic departments or
consider sharing a data scientist. So it can’t be emphasized enough that people
are at the heart of the discipline, not data.

The biggest gains
from big data impact are in customer-facing areas. The report recommends
that social media and web-tracking technologies are especially useful for
collecting customer data. Loyalty cards and user-generated web content have
already led to significant changes in retail and entertainment. Of course,
consumer products companies have long used customer data supplied by
information and measurement companies like Nielson. Thus, marketing and sales groups
have built-in experience and history of using customer generated data, making
analogies to web-data an easier reach for those firms.

Evidence of the use and advance of data is everywhere. As
the report indicates, an ecosystem of
data-focused companies is springing up and creating new businesses. Still
further, analysts at the McKinsey Global
Institute estimate that just one sector—the US healthcare industry—could create
US$300bn in value annually if it used big data to drive efficiency and quality.

The “why” of big data is fairly obvious. It’s the “how”
that creates and will continue to create frustration, while at the same time
creating significant business success. That’s typical of an evolving
discipline.